Buffon's needle experiment for estimating π is a classical example of using an experiment (or a simulation) to estimate a probability. This example is presented in many books on statistical simulation and is famous enough that Brian Ripley in his book Stochastic Simulation states that the problem is "well known
One aspect of blogging that I enjoy is getting feedback from readers. Usually I get statistical or programming questions, but every so often I receive a comment from someone who stumbled across a blog post by way of an internet search. This morning I received the following delightful comment on
I was contacted by SAS Technical Support regarding a customer who was trying to use SAS/IML to compute quantiles of the folded normal distribution. I had heard of the distribution, but it is not built into SAS and I had never worked with it. Nevertheless, I set out to understand
When I learn a new statistical technique, one of first things I do is to understand the limitations of the technique. This blog post shares some thoughts on modeling finite mixture models with the FMM procedure. What is a reasonable task for FMM? When are you asking too much? I
Normal, Poisson, exponential—these and other "named" distributions are used daily by statisticians for modeling and analysis. There are four operations that are used often when you work with statistical distributions. In SAS software, the operations are available by using the following four functions, which are essential for every statistical programmer
Sometimes a population of individuals is modeled as a combination of subpopulations. For example, if you want to model the heights of individuals, you might first model the heights of males and females separately. The height of the population can then be modeled as a combination of the male and
I previously showed how to generate random numbers in SAS by using the RAND function in the DATA step or by using the RANDGEN subroutine in SAS/IML software. These functions generate a stream of random numbers. (In statistics, the random numbers are usually a sample from a distribution such as
You can generate a set of random numbers in SAS that are uniformly distributed by using the RAND function in the DATA step or by using the RANDGEN subroutine in SAS/IML software. (These same functions also generate random numbers from other common distributions such as binomial and normal.) The syntax
I recently blogged about how many times, on average, you must roll a die until you see all six faces. This question is a special case of the coupon collector's problem. My son noted that the expected value (the mean number of rolls) is not necessarily the best statistic to
As I was reviewing notes for my course "Data Simulation for Evaluating Statistical Methods in SAS," I realized that I haven't blogged about simulating categorical data in SAS. This article corrects that oversight. An Easy Way and a Harder Way SAS software makes it easy to sample from discrete "named"
